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Introduction

Column {.tabset}

Background

Column {.tabset}

=======

Introduction

Column {.tabset}

Background

Column {.tabset}

>>>>>>> main

Approach of Project

The report will be collating specific sections of this data pertaining to countrywise policy responses, consumption pattern, youth and alcohol, levels of consumption and harms and consequences.

Information on Data

This report is being created using the data from the GISAH (Global Information System on Alcohol and Health) tool of the World Health Organisation. GISAH tool helps monitor trends relating to alcohol consumption across various age groups, side effects (harm caused) as well as the policy responses by different countries.

Makers

Team Name: Hayaku

Researchers:

  • Kexin Xu
  • Smriti Vinayak Bhat
  • Yalong liu
  • Yin Shan Ho
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Popularity

Column

Total Consumption

consumption <- read_csv("../clean_data/consumption_join.csv")
# Origin data pivot longer

b_tidy <- consumption %>%
  filter(Beverage_Types == "Spirits") %>%
  pivot_longer(cols = -c(Country, Beverage_Types),
               names_to = "Year",
               values_to = "Litres")
b_tidy
# A tibble: 11,210 x 4
   Country     Beverage_Types Year  Litres
   <chr>       <chr>          <chr>  <dbl>
 1 Afghanistan Spirits        2018    0.01
 2 Afghanistan Spirits        2017    0.01
 3 Afghanistan Spirits        2016    0.01
 4 Afghanistan Spirits        2015    0   
 5 Afghanistan Spirits        2014    0.01
 6 Afghanistan Spirits        2013    0   
 7 Afghanistan Spirits        2012    0   
 8 Afghanistan Spirits        2011    0   
 9 Afghanistan Spirits        2010    0   
10 Afghanistan Spirits        2009    0   
# … with 11,200 more rows
# Loading countries data
countries <- ne_countries(returnclass = "sf", scale = "medium") %>%
  select(admin, continent, pop_est, gdp_md_est, economy, income_grp )
# Test unmatched countries

anti_join(b_tidy, countries, by = c("Country" = "admin"))
# A tibble: 1,416 x 4
   Country Beverage_Types Year  Litres
   <chr>   <chr>          <chr>  <dbl>
 1 Bahamas Spirits        2018    3.78
 2 Bahamas Spirits        2017    3.74
 3 Bahamas Spirits        2016    3.6 
 4 Bahamas Spirits        2015    3.61
 5 Bahamas Spirits        2014    3.54
 6 Bahamas Spirits        2013    3.46
 7 Bahamas Spirits        2012    3.56
 8 Bahamas Spirits        2011    3.68
 9 Bahamas Spirits        2010    3.69
10 Bahamas Spirits        2009    3.69
# … with 1,406 more rows
# Modify the country name consisting with rnaturalearth package

b_l_country <- b_tidy %>%
  mutate(Country = recode(Country,
                             "Bahamas" = "The Bahamas",
                             "Bolivia (Plurinational State of)" = "Bolivia",
                             "Brunei Darussalam" = "Brunei",
                             "Cabo Verde" = "Cape Verde",
                             "Congo" = "Democratic Republic of the Congo",
                             "Côte d'Ivoire" = "Ivory Coast",
                             "Czechia" = "Czech Republic",
                             "Democratic People's Republic of Korea" = "North Korea",
                             "Eswatini" = "Swaziland",
                             "Guinea-Bissau" = "Guinea Bissau",
                             "Iran (Islamic Republic of)" = "Iran",
                             "Lao People's Democratic Republic" = "Laos",
                             "Micronesia (Federated States of)" = "Federated States of Micronesia",
                             "North Macedonia" = "Macedonia",
                             "Republic of Korea" = "South Korea",
                             "Republic of Moldova" = "Moldova",
                             "Russian Federation" = "Russia",
                             "Serbia" = "Republic of Serbia",
                             "Syrian Arab Republic" = "Syria",
                             "Timor-Leste" = "East Timor",
                             "Tuvalu" = "Wallis and Futuna",
                             "United Kingdom of Great Britain and Northern Ireland" = "United Kingdom", 
                             "Venezuela (Bolivarian Republic of)" = "Venezuela",
                             "Viet Nam" = "Vietnam"))%>%
  filter(!is.na(Country))
# Beer popularity by time

b_l_country_time <- left_join(countries, b_l_country, by = c("admin" = "Country"))

b_time  <- ggplot()+
  geom_sf(data = countries, fill = NA)+
  geom_sf(data = b_l_country_time %>% filter(!is.na(Year)), mapping = aes(fill = Litres))+
  scale_fill_viridis(na.value="white")+
  labs(title = "Countries where Beer is popular",
       subtitle = "Year: {current_frame}",
       fill = "Total")+
  theme_bw()+
  transition_manual(Year)


animate(b_time, duration = 30, fps = 20, width = 1000, height = 500, renderer = gifski_renderer())

Total Percentage

pie

consumption %>%
  select(Beverage_Types, "2018") %>%
  group_by(Beverage_Types) %>%
  summarise(no = sum(`2018`, na.rm = TRUE))
# A tibble: 5 x 2
  Beverage_Types               no
  <chr>                     <dbl>
1 All types                 902. 
2 Beer                      372. 
3 Other alcoholic beverages  97.6
4 Spirits                   265. 
5 Wine                      174. 
# The number of litres consumed for each country

b_l_country <- b_tidy %>%
  group_by(Country)%>%
  summarise(Litres = sum(Litres, na.rm = TRUE))
b_l_country
# A tibble: 189 x 2
   Country             Litres
   <chr>                <dbl>
 1 Afghanistan           0.08
 2 Albania              66.5 
 3 Algeria               3   
 4 Andorra              50.3 
 5 Angola               35.4 
 6 Antigua and Barbuda 162   
 7 Argentina            74.9 
 8 Armenia              77.3 
 9 Australia            85.7 
10 Austria             105.  
# … with 179 more rows

%>% group_by(Country, Year)%>% summarise(Litres = sum(Litres, na.rm = TRUE))

Research Question 1

How has the popularity of each type of alcohol changed over the years for different countries and have the local favorites of earlier decades been overtaken by other types of alcohol in terms of consumption?

>>>>>>> Added the map of beer consumption
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Popularity

Cause of Mortality

Column {.tabset}

Overview on the alcohol-attributed death

Comparison between alcohol-attributed deaths

Calculation Method:

  • Cancer Death \(\times\) Alcohol-Attributed Cancer Death Rate

  • Liver Cirrhosis Death \(\times\) Alcohol-attributed Liver Cirrhosis Death Rate

  • Road Traffic Death \(\times\) Alcohol-attributed Road Traffic Death Rate

  • Sum up all types of alcohol-attributed deaths

Analysis:

  • Alcohol causes the highest death rate in Nigeria and the lowest in Lybia.

  • Alcohol brings the highest number of deaths in African countries while the lowest in Middle East countries.

  • Alcohol causes more deaths to Males.

  • Liver Cirrhosis causes the highest death rate while Road Traffic Accidents causes the lowest.

Possible reasons affecting death rates

  • Religions

  • Income

  • Education

Column {.tabset}

Total number of alcohol attributed death

Alcohol-Attributed Death VS Road Traffic death (By Income Level)

income group alcohol-attribute death road traffic death
low 28.25030 44.315714
upper_middle 24.83446 20.202222
lower_middle 24.16239 25.591026
high 18.70564 9.362195

Comparison between total alcohol-attributed deaths and traffic deaths

Harms of alcohol

  • Cancer

  • Liver Cirrhosis

  • Road traffic accidents

Calculation method:

  • Average number of deaths(per 100,000 population) in each continent.

Analysis:

  • Alcohol causes more deaths to Males.

  • Liver Cirrhosis causes the highest death rate while Road Traffic Accidents causes the lowest.

Policy Pattern

=======

Popularity

Cause of Mortality

Policy Pattern

Column {.tabset} {data-width = 400}

Infographic of Policies

>>>>>>> main

<<<<<<< HEAD

Age Restrictions

Youth Inebriation

Limitations

References

=======

Effectiveness of Policy Actions

Column {.tabset} {data-width = 650}

Policies being favored by countries across years 2010-2016

Analysis

  • There are 10 main areas of Policy Actions that the effectiveness are measured for. From the figure of Policies favored across countries from the years 2010-2016, it is visible that ‘Leadership, awareness and commitment’ policies are the ones that are being favored by most countries.
  • Upon checking the largest decreases in per capita alcohol consumption it is visible that this policy has only 1 entry.
  • However upon checking the largest increases in per capita alcohol consumption it is visible that this policy has 5 entries.
  • This shows that the governments of the different countries may not be taking the most efficient of routes.
    Action Area mean
    Reducing the negative consequences of drinking and alcohol intoxication -0.1967742
    Monitoring and surveillance -0.1297297
    Pricing policies -0.1004082
    Reducing the public health impact of illicit alcohol and informally produced alcohol -0.0850000
    Availability of alcohol -0.0640541
    Drink-driving policies and countermeasures -0.0428125
    Health services’ response -0.0420290
    Community and workplace action -0.0132000
    Marketing of alcoholic beverages -0.0072727
    Leadership, awareness and commitment 0.0222872
  • From the table we can see that the most efficient action area seems to be - Reducing the negative consequences of drinking and alcohol intoxication
  • While the least efficient area is - Leadership, awareness and commitment
  • One area of relief is that the second most efficent action area i.e., Monitoring and surveillance, is also the second most used strategy by countries across the world.

The biggest dip in consumption was seen in Mongolia in the year 2013 after the step - International alcohol study..

The biggest spike in consumption was seen in Spain in the year 2014 after the step - Si estás embarazada, con el alcohol no hay excusas [If you are pregrant, there is no excuse for alcohol]. Menores ni una gota de alcohol [Minors, not a drop of alcohol]. Media campaigns aimed at both minors and pregnant women implemented by Ministerio de Sanidad [Ministry of Health] and Federación Española de Bebidas Espirituosas [Spanish Federation of Spirit-based Beverages].


Age Restrictions

Youth Inebriation

Limitations

Column

Popularity

Cause of Mortality

Policy Patterns

  1. The values for each policy area have not been weighted by importance.
  2. Data is not available for a large number of countries so the analysis has been performed on the limited data set given.
  3. This analysis has only been limited between the years of 2010-2016.
  4. There could be population/age bias as demographic data is unavailable.

Age Restrictions

Youth Inebriation

References

>>>>>>> main

GHO | global information system on alcohol and Health (gisah) | global information system on alcohol and health. (n.d.). Retrieved May 09, 2021, from https://apps.who.int/gho/data/node.gisah.GISAH?lang=en&showonly=GISAH

---
title: "Analysis of Alcohol Consumption Patterns"
output:
  flexdashboard::flex_dashboard:
    vertical_layout: scroll
    theme: bootstrap
    storyboard: true
    source_code: embed
    navbar:
      - { title: "Hayaku",  align: right}
      - { title: "Kexin Xu, Smriti Vinayak Bhat, Yalong liu, Yin Shan Ho",  align: right}
---
   

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(knitr)
library(tidytuesdayR)
library(tidyverse)
library(sf)
library(rnaturalearth)
library(gganimate)
library(viridis)
library(RColorBrewer)
library(rgeos)
library(gifski)
```

Introduction {.tabset}
======================

Column {.tabset}
-------------------------------------

### Background




Column {.tabset} {data-width=100}
-------------------------------------

### Approach of Project

The report will be collating specific sections of this data pertaining to countrywise policy responses, consumption pattern, youth and alcohol, levels of consumption and harms and consequences.

### Information on Data

This report is being created using the data from the GISAH (Global Information System on Alcohol and Health) tool of the World Health Organisation. GISAH tool helps monitor trends relating to alcohol consumption across various age groups, side effects (harm caused) as well as the policy responses by different countries.


Popularity {.tabset}
======================

Column {data-width=400}
-------------------------------------

### Total Consumption
```{r}
consumption <- read_csv("../clean_data/consumption_join.csv")
```

```{r} 
# Origin data pivot longer

b_tidy <- consumption %>%
  filter(Beverage_Types == "Spirits") %>%
  pivot_longer(cols = -c(Country, Beverage_Types),
               names_to = "Year",
               values_to = "Litres")
b_tidy
```

```{r}
# Loading countries data
countries <- ne_countries(returnclass = "sf", scale = "medium") %>%
  select(admin, continent, pop_est, gdp_md_est, economy, income_grp )
```



```{r}
# Test unmatched countries

anti_join(b_tidy, countries, by = c("Country" = "admin"))
```
```{r}
# Modify the country name consisting with rnaturalearth package

b_l_country <- b_tidy %>%
  mutate(Country = recode(Country,
                             "Bahamas" = "The Bahamas",
                             "Bolivia (Plurinational State of)" = "Bolivia",
                             "Brunei Darussalam" = "Brunei",
                             "Cabo Verde" = "Cape Verde",
                             "Congo" = "Democratic Republic of the Congo",
                             "Côte d'Ivoire" = "Ivory Coast",
                             "Czechia" = "Czech Republic",
                             "Democratic People's Republic of Korea" = "North Korea",
                             "Eswatini" = "Swaziland",
                             "Guinea-Bissau" = "Guinea Bissau",
                             "Iran (Islamic Republic of)" = "Iran",
                             "Lao People's Democratic Republic" = "Laos",
                             "Micronesia (Federated States of)" = "Federated States of Micronesia",
                             "North Macedonia" = "Macedonia",
                             "Republic of Korea" = "South Korea",
                             "Republic of Moldova" = "Moldova",
                             "Russian Federation" = "Russia",
                             "Serbia" = "Republic of Serbia",
                             "Syrian Arab Republic" = "Syria",
                             "Timor-Leste" = "East Timor",
                             "Tuvalu" = "Wallis and Futuna",
                             "United Kingdom of Great Britain and Northern Ireland" = "United Kingdom", 
                             "Venezuela (Bolivarian Republic of)" = "Venezuela",
                             "Viet Nam" = "Vietnam"))%>%
  filter(!is.na(Country))
```

```{r}
# Beer popularity by time

b_l_country_time <- left_join(countries, b_l_country, by = c("admin" = "Country"))

b_time  <- ggplot()+
  geom_sf(data = countries, fill = NA)+
  geom_sf(data = b_l_country_time %>% filter(!is.na(Year)), mapping = aes(fill = Litres))+
  scale_fill_viridis(na.value="white")+
  labs(title = "Countries where Beer is popular",
       subtitle = "Year: {current_frame}",
       fill = "Total")+
  theme_bw()+
  transition_manual(Year)


animate(b_time, duration = 30, fps = 20, width = 1000, height = 500, renderer = gifski_renderer())
```


### Total Percentage

*pie*

```{r}
consumption %>%
  select(Beverage_Types, "2018") %>%
  group_by(Beverage_Types) %>%
  summarise(no = sum(`2018`, na.rm = TRUE))
```

```{r}
# The number of litres consumed for each country

b_l_country <- b_tidy %>%
  group_by(Country)%>%
  summarise(Litres = sum(Litres, na.rm = TRUE))
b_l_country
```

%>%
  group_by(Country, Year)%>%
  summarise(Litres = sum(Litres, na.rm = TRUE))


### Research Question 1 

**How has the popularity of each type of alcohol changed over the years for different countries and have the local favorites of earlier decades been overtaken by other types of alcohol in terms of consumption?**

### Research Question 2 

**Does alcohol take more lives in traffic crashes or illnesses and which countries suffer more from alcohol-related cases?**

### Research Question 3

**What are the recurring patterns of policy responses across different countries in the world and how has it impacted the consumption of alcohol?**

### Research Question 4

**Do drinking age restrictions and legal blood alcohol concentration (BAC) limits for young drivers have any effect on adolescents’ drinking among countries?**

### Research Question 5

**What are the trends in youth drinking issues over the years?**


Findings {.tabset}
======================

Limitations {.tabset}
======================

Conclusion {.tabset}
======================

References {.tabset}
======================

GHO | global information system on alcohol and Health (gisah) | global information system on alcohol and health. (n.d.). Retrieved May 09, 2021, from https://apps.who.int/gho/data/node.gisah.GISAH?lang=en&showonly=GISAH